Pandas DataFrame Methods equals(), filter(), first(), last(), head(), and tail()

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The Pandas DataFrame has several Re-indexing/Selection/Label Manipulations methods. When applied to a DataFrame, these methods evaluate, modify the elements and return the results.

This is Part 7 of the DataFrame methods series:

Part 1 focuses on the DataFrame methods abs(), all(), any(), clip(), corr(), and corrwith().
Part 2 focuses on the DataFrame methods count(), cov(), cummax(), cummin(), cumprod(), cumsum().
Part 3 focuses on the DataFrame methods describe(), diff(), eval(), kurtosis().
Part 4 focuses on the DataFrame methods mad(), min(), max(), mean(), median(), and mode().
Part 5 focuses on the DataFrame methods pct_change(), quantile(), rank(), round(), prod(), and product().
Part 6 focuses on the DataFrame methods add_prefix(), add_suffix(), and align().
Part 7 focuses on the DataFrame methods at_time(), between_time(), drop(), drop_duplicates() and duplicated().
Part 8 focuses on the DataFrame methods equals(), filter(), first(), last(), head(), and tail()

Getting Started

Note: To follow along with the examples below, click here to download the finxters.csv file of auto-generated dummy user data. Move this file to the current working directory.

Remember to add the Required Starter Code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

Required Starter Code

import pandas as pd

Before any data manipulation can occur, this library will require installation:

The pandas library enables access to/from a DataFrame.

To install these libraries, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

Feel free to check out the correct ways of installing the library here:

If the installations were successful, a message displays in the terminal indicating the same.

DataFrame equals()

The equals() method compares two (2) DataFrames/Series against each other to determine if they have an identical shape and elements. If identical return True, otherwise return False.

The syntax for this method is as follows:

DataFrame.equals(other)

Parameter	Description
`other`	A DataFrame or Series to compare.

For this example, we have two (2) DataFrames containing grades for three (3) students.

df_scores1 = pd.DataFrame({'Micah': [91, 58, 73],
                           'Bob':     [53, 87, 46],
                           'Chloe':  [60, 54, 61]})
  
df_scores2 = pd.DataFrame({'Micah': [91, 58, 73],
                           'Bob':     [53, 87, 46],
                           'Chloe':  [60, 54, 61]})
  
result = df_scores1.equals(df_scores2)
print(result)
print(df_scores1.shape)
print(df_scores2.shape)

Line [1-2] creates two (2) DataFrames.
Line [3] compares df_scores1 against df_scores2 testing the shape and elements. The outcome saves to result (True/False).
Line [4] outputs the result to the terminal.
Line [5-6] confirms the shape of the DataFrames is equal by outputting the results to the terminal.

Output:

True
(3, 3)
(3, 3)

DataFrame filter()

The filter() method returns a subset of a DataFrame/Series rows/columns based on index label(s). This method does not filter a DataFrame/Series on the contents. The filter applies to the specific label(s) of the selected index. The return value is a subset of the callable.

The syntax for this method is as follows:

DataFrame.filter(items=None, like=None, regex=None, axis=None)

Parameter	Description
`items`	A filter list of columns to include in the result.
`like`	A filter string of columns (ex: `like='FID'`) to include in the result.
`regex`	A regex string to filter columns (ex: `regex='e$'`) to include in the result.
`axis`	If zero (0) or index is selected, apply to each column. Default is `None`. If one (1) is selected, apply to each row.

For this method, various scenarios below highlight its capabilities.

This example uses the items parameter to filter the DataFrame and return the result.

Code – Example 1:

df_fxs = pd.read_csv('finxters.csv')
result = df_fxs.filter(items=['Username', 'Rank'])
print(result.head())

Line [1] reads in the comma-separated CSV file and saves it to df_fxs.
Line [2] filters df_fxs to include only the columns matching the item labels (Username and Rank). These columns (and associated values) save to result.
Line [3] outputs the first five (5) rows (head()) to the terminal.

Output:

	Username	Rank
0	wildone92	Authority
1	AmyP	Beginner
2	1998_pete	Basic Knowledge
3	TheCoder	Experienced Learner
4	AliceM	Authority

This example uses the like parameter to filter the DataFrame and return the result.

Code – Example 2:

df_fxs = pd.read_csv('finxters.csv')
result = df_fxs.filter(like='FID', axis=1)
print(result.head())

Line [1] reads in the comma-separated CSV file and saves it to df_fxs.
Line [2] filters df_fxs to include only the columns matching the like label ('FID'). This column (and associated values) save to result.
Line [3] outputs the first five (5) rows (head()) to the terminal.

Output:

	FID
0	30022145
1	30022192
2	30022331
3	30022345
4	30022359

This example uses the regex parameter to filter the DataFrame and return the result.

Code – Example 3

df_fxs = pd.read_csv('finxters.csv')
result = df_fxs.filter(regex='e$', axis=1)
print(result.head())

Line [1] reads in the comma-separated CSV file and saves it to df_fxs.
Line [2] filters df_fxs to include only the columns matching the regex label (First_Name, Last_Name and Username). These columns (and associated values) save to result.
Line [3] outputs the first five (5) rows (head()) to the terminal.

Output:

	First_Name	Last_Name	Username
0	Steve	Hamilton	wildone92
1	Amy	Pullister	AmyP
2	Peter	Dunn	1998_pete
3	Marcus	Williams	TheCoder
4	Alice	Miller	AliceM

Note: Each column name in the output ends with the letter e.

DataFrame first()

The first() method retrieves and returns the first set number of rows (periods) based on the value entered. The index must be a date value to return the appropriate results.

The syntax for this method is as follows:

DataFrame.first(offset)

Parameter	Description
`offset`	This parameter is the date period of the data to display (ex: 1M, 2D).

For this example, the blood pressure for three (3) patients over a two (2) month period is retrieved.

r = pd.date_range('2021-01-01', periods=3, freq='1M')
df = pd.DataFrame({'Patient-1': [123, 120, 144], 
                   'Patient-2': [129, 125, 90],
                   'Patient-3': [101, 95,  124]},index=r)

result = df.first('1M')
print(result)

Line [1] sets up the following:
- The date range start date ('2021-01-01').
- The number of periods (3).
- The frequency ('1M'). This statement equates to 1 Month.
Line [2] creates a DataFrame containing:
- Three (3) patient names containing three (3) elements of data for each patient.
Line [3] saves the first month period to result.
Line [4] outputs the result to the terminal.

Output:

	Patient-1	Patient-2	Patient-3
2022-01-31	123	129	101
2022-02-28	120	125	95

Note: The date range for the selected frequency references the last day of the month.

DataFrame last()

The last() method retrieves and returns the last set number of rows (periods) based on the value entered. The index must be a date value for this method to return the expected results.

The syntax for this method is as follows:

DataFrame.last(offset)

Parameter	Description
`offset`	This parameter is the date period of the data to display (ex: 1M, 2D).

For this example, the blood pressure for three (3) patients over a two (2) month period is retrieved.

r = pd.date_range('2021-01-01', periods=3, freq='1M')
df = pd.DataFrame({'Patient-1': [123, 120, 144], 
                   'Patient-2': [129, 125, 90],
                   'Patient-3': [101, 95,  124]},index=r)

result = df.last('1M')
print(result)

Line [1] sets up the following:
- The date range start date ('2021-01-01').
- The number of periods (3).
- The frequency ('1M'). This statement equates to 1 Month.
Line [2] creates a DataFrame containing:
- Three (3) patient names containing three (3) elements of data for each patient.
Line [3] saves the output to result.
Line [4] outputs the result to the terminal.

Output:

	Patient-1	Patient-2	Patient-3
2022-03-31	144	90	125

Note: The date range for the selected frequency references the last day of the month.

DataFrame head() and tail()

These methods display n numbers of records (top or bottom). These methods are useful when you are accessing large amounts of data.

If no parameter is entered, by default, five (5) rows display.

The head() method returns the top five (5) rows of the DataFrame.
The tail() method returns the bottom five (5) rows of the DataFrame

If a parameter is entered in one of the above methods, n number of rows (top/bottom) will display.

As you will note, the head() method was accessed many times during this article. Both methods are must-haves in your knowledge base.

The syntax for these methods is as follows:

DataFrame.head(n=5)
DataFrame.tail(n=5)

Parameter	Description
`n`	An integer value indicating the number of rows to display. By default, five (5) rows display.

For this example, the first five (5) records from the DataFrame and the last five (5) records (tail) from the same DataFrame display.

df_fxs = pd.read_csv('finxters.csv')
print(df_fxs.head())
print(df_fxs.tail())

Output:

Finxter